Info

This report has been automatically generated by the mspms package. It contains standard results of the mspms analysis pipeline.

R session info is as follows:

sessionInfo()
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS 13.1
## 
## Matrix products: default
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] mspms_0.99.0    lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
##  [6] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1  
## [11] tidyverse_2.0.0
## 
## loaded via a namespace (and not attached):
##  [1] bslib_0.7.0       ggprism_1.0.5     tidyselect_1.2.1  xfun_0.44         carData_3.0-5    
##  [6] colorspace_2.1-0  vctrs_0.6.5       generics_0.1.3    htmltools_0.5.8.1 viridisLite_0.4.2
## [11] yaml_2.3.8        utf8_1.2.4        rlang_1.1.4       jquerylib_0.1.4   ggpubr_0.6.0     
## [16] pillar_1.9.0      glue_1.7.0        withr_3.0.0       bit64_4.0.5       lifecycle_1.0.4  
## [21] munsell_0.5.1     ggsignif_0.6.4    gtable_0.3.5      htmlwidgets_1.6.4 evaluate_0.24.0  
## [26] knitr_1.47        tzdb_0.4.0        fastmap_1.2.0     parallel_4.1.1    fansi_1.0.6      
## [31] broom_1.0.6       backports_1.5.0   scales_1.3.0      DT_0.33           cachem_1.1.0     
## [36] jsonlite_1.8.8    vroom_1.6.5       abind_1.4-5       bit_4.0.5         gridExtra_2.3    
## [41] hms_1.1.3         digest_0.6.35     stringi_1.8.4     rstatix_0.7.2     grid_4.1.1       
## [46] cli_3.6.2         tools_4.1.1       sass_0.4.9        magrittr_2.0.3    crayon_1.5.2     
## [51] car_3.1-2         pkgconfig_2.0.3   timechange_0.3.0  rmarkdown_2.27    rstudioapi_0.16.0
## [56] viridis_0.6.5     R6_2.5.1          compiler_4.1.1

Data Processing

# The standard mspms data processing pipeline is used here. 
mspms_data = mspms::mspms(prepared_data,design_matrix)

mspms_data %>%
  downloadthis::download_this(
    output_name = "mspms_data",
    output_extension = ".csv",
    button_label = "Download data",
    button_type = "warning",
    has_icon = TRUE,
    icon = "fa fa-save",
    csv2 = FALSE
  )

QC Checks

qc_checks = mspms::qc_check(prepared_data,peptide_library,design_matrix)

DT::datatable(qc_checks)
qc_checks %>%
  downloadthis::download_this(
    output_name = "qc_checks",
    output_extension = ".csv",
    button_label = "Download qc_check",
    button_type = "warning",
    has_icon = TRUE,
    icon = "fa fa-save",
    csv2 = FALSE
  )

Table 1. Here the percentage of the peptide library that is detected in each sample is displayed.

mspms::plot_qc_check(qc_checks)
## [[1]]

## 
## [[2]]

Figure 1. Here the results of the qc checks are displayed.

nd_peptides = mspms::find_nd_peptides(prepared_data,
                                      peptide_library,
                                      design_matrix)

DT::datatable(nd_peptides)
nd_peptides %>%
  downloadthis::download_this(
    output_name = "nd_peptides",
    output_extension = ".csv",
    button_label = "Download nd_peptides",
    button_type = "warning",
    has_icon = TRUE,
    icon = "fa fa-save",
    csv2 = FALSE
  )

Table 2. Here the peptides missing from each sample are shown

plot_nd_peptides(nd_peptides)

Figure 2. Here the library peptides not detected in each samples are shown.

plot_rt_qc(prepared_data,design_matrix)

Figure 3. Here the distribution of peptides detected per retention time are shown for each group.

Overview

mspms::plot_pca(mspms_data)

Figure 4. Here a PCA of the experiment is displayed. The color represents time, while shape and linetype of eclipse (if there are enough points to calculate) represents condition.

mspms::plot_heatmap(mspms_data)

Figure 5. Here an interactive heatmap of the experiment is displayed. Mouse over to see more information on each cell. Column side colors represent condition and time.

Statistics

t1 = mspms::mspms_anova(mspms_data) %>% 
  dplyr::arrange(p.adj)

DT::datatable(t1)
t1 %>%
  downloadthis::download_this(
    output_name = "mspms_anova",
    output_extension = ".csv",
    button_label = "Download anova",
    button_type = "warning",
    has_icon = TRUE,
    icon = "fa fa-save",
    csv2 = FALSE
  )

Table 3. Here anova results are displayed for each condition (testing to see if there is an effect on time).

t2 = mspms::log2fc_t_test(mspms_data) %>% 
  dplyr::arrange(p.adj)

DT::datatable(t2)
t2 %>%
  downloadthis::download_this(
    output_name = "mspms_t-tests",
    output_extension = ".csv",
    button_label = "Download ttests",
    button_type = "warning",
    has_icon = TRUE,
    icon = "fa fa-save",
    csv2 = FALSE
  )

Table 4. Here T-test results are displayed as compared to time 0 for each condition

mspms::plot_volcano(t2)

Figure 5. Here the Log2fc and T tests are displayed as volcano plots relative to T0 for each condition.

Cleavage Motifs

tryCatch({
  print(mspms::plot_all_icelogos(mspms_data))
}, error = function(e) {
  print(e)
})

Figure 6. Here the cleavage motifs enriched in each condition are shown. Enriched cleavage motifs are defined as having a p.adj <= 0.05 and a log2fc >3 relative to time 0.

Position Specificity

sig = t2 %>% 
  filter(p.adj <= 0.05, log2fc >3)

d = mspms::count_cleavages_per_pos(sig)

tryCatch({
  print(mspms::plot_cleavages_per_pos(d))
}, error = function(e) {
  print(e)
})

Figure 7. Here the count of significant cleavages at each position of the peptide library are shown for each condition (facet) per time point (color). Significant cleavages are defined as having a p.adj <= 0.05 and a log2fc > 3 relative to time 0.